@Marvin how can I use a progress bar with prefect 3.X to check up on the progress of multiple tasks being run in parallel?

It seems that there isn't a direct mention of implementing a progress bar in the Prefect 3.x documentation for monitoring the progress of tasks running in parallel. However, you can achieve this by integrating a third-party library such as

tqdm

with your Prefect flow. To track the progress of multiple tasks running in parallel, you might need to manually update your progress bar within each task or after the execution of a batch of tasks. You can use a shared state or a queue to collect progress information from each task, which can then be used to update the progress bar. Here’s a rough idea of how you might do this using `tqdm`: 1. Initialize a

tqdm

progress bar before starting your flow. 2. Run your tasks in parallel as usual. 3. Inside each task, update the progress bar. You might need to use a shared lock or some threading/multiprocessing mechanism to safely update the progress bar from multiple parallel tasks. You can check more detailed integrations and examples through the Prefect documentation or look through community forums and examples online for specific implementations using Prefect with a progress bar. Here's a general resource on running tasks in parallel with workers: https://docs-3.prefect.io/v3/develop/deferred-tasks. If you'd like to explore further or need a specific example, just let me know!

@Marvin write an example flow that runs the same task 10 times in parallel and use a progress bar to show the progress of the flow.

It appears that the Prefect 3.x documentation doesn't have a direct example of using a progress bar with parallel task execution. However, you can certainly achieve this using Prefect alongside the

tqdm

library. Below is a fictional example of how you might implement a flow that runs a task 10 times in parallel, accompanied by a progress bar:

python
from prefect import flow, task
from tqdm import tqdm
import time
import concurrent.futures

@task
def my_task(task_number):
    time.sleep(1)
    return f"Task {task_number} completed."

@flow
def parallel_flow():
    # Create a thread pool for parallel execution with 10 workers
    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
        futures = []
        progress = tqdm(total=10)  # Initialize the progress bar

        # Submit tasks
        for i in range(10):
            future = executor.submit(my_task.run, task_number=i)
            future.add_done_callback(lambda p: progress.update())
            futures.append(future)

        # Wait for all futures to complete
        results = [future.result() for future in futures]
        progress.close()

        for result in results:
            print(result)

# Run the flow
if __name__ == "__main__":
    parallel_flow()

### How it Works - We're using

concurrent.futures.ThreadPoolExecutor

for running tasks in parallel. Each task simply waits for one second and returns a message. -

tqdm

is used for the progress bar, updated each time a task completes. - As each future (representing a task execution) completes, a callback updates the progress bar. To adapt the above code for actual Prefect usage, ensure Prefect is set up properly in your environment. If you'd like, I can help with more specific deployment or integration questions! And remember, watching progress bars is a bit like watching a kettle boil – oddly satisfying!